Electrical utilities must continually manage their capacity to ensure that the amount of electricity generated by the utility, or purchased from other utilities, is sufficient to meet the load demand placed on the system by their customers. Utilities generally have two options for meeting demands on the system during periods of peak energy demand (loading). These include either bringing additional generating capacity on-line to satisfy the increased demand; or, if properly equipped, load control (LC) to selectively shed load across their customer base to reduce overall demand on the system.
Demand response thus refers to the reduction of a customer's energy usage at times of peak demand. It is done for a variety of reasons including system reliability (the avoidance of “blackouts” or “brownouts”), market conditions and pricing (preventing the utility from having to buy additional energy on the open-market at premium prices), and supporting infrastructure optimization or deferral. Demand response programs include dynamic pricing/tariffs, price-responsive demand bidding, contractually obligated and voluntary curtailment of energy usage, and direct load control/cycling.
There is a trend in the electric utility industry, with respect to performing load control (LC) on appliances, that the LC should be based on the individual appliance loading characteristics. Indeed this provides more equitable load shedding for a given consumer and a finer resolution of control based on the end consumer's profiled load. When performing LC in this fashion the LC algorithms are based on individual local information which provides some variability with respect to the aggregate loading that the utility observes. Developing these LC devices to perform their functions based on local information is attractive because it can reduce the burden on the utility with respect to formation and execution of an LC event. They no longer have to perform detailed modeling or analysis prior to LC execution to achieve their desired goals. They may simply specify a certain level of load they wish to dispatch and each device targeted by that LC event will dispatch that percentage of its load thus contributing to the aggregate load reduction. However; when specifying local rules or behaviors for cycling a load, new behaviors can emerge at a global level. These emergent behaviors may be benign, positive or negative. One such emergent behavior that has a negative impact on the utility is termed “false peaking”.
False peaking is when a significant enough portion of a population of loads, currently under an LC event, allow load to run at the same moment in time such that the demand, as seen by the utility, raises to an undesirable level. Sometimes this false peak may be as bad as or worse than the original anticipated peak demand that the LC event sought to eliminate. In global based LC patterns where every LC device is executing a pattern imposed on them regardless of the local behavior of the load, false peaking is not a threat as the utility will construct the cycling behavior to ensure that a certain percentage of the loads are always off. When allowing each individual LC device to determine its own cycling pattern independent of the other devices, there is no global guarantee that at a given time all the loads will not happen to cycle on. A certain amount of variability within the characteristics of the load, the LC device settings, etc. will help to reduce the likelihood of false peaking in a passive fashion, but that does not actively set out to reduce false peak occurrences.
To better understand the role that local rule based LC using local information has on false peaking, one must first understand how the local load information is generally structured and how it relates to the cycling of a load during a LC event.
Generally, the load to be controlled is characterized based on its usage patterns or habits. This load characterization is also known as a usage profile. Typically the profiles will be discretized such that the desirable level of usage resolution is achieved while minimizing data storage requirements. This resolution is typically granular enough that the usage profile provides an accurate representation of the load over a small period of time. However, during a LC event the load will be cycled over a period of time generally much larger than the resolution of the accumulated usage profile. Therefore the formation of even a single LC cycle (opening and closing a relay, or simply ON and OFF) likely uses data from multiple elements of the profile. Therefore in order to compute how the load must cycle, there is generally an aggregation step that involves the grouping of the usage profile elements into periods of time suitable for computing a cycling pattern. Also note that over a given LC event, there may be multiple periods of time in which different cycling patterns are computed. This allows a single event to contain cycling patterns that vary over time to better match the typical usage behavior of the load.
This invention relates to load control of pieces of equipment connected to an electrical distribution system. More particularly, the invention includes an apparatus and method of performing load control of the pieces of equipment based on load characterizations of the pieces of equipment. The apparatus and method employ cyclical and non-cyclical peak reduction in the aggregate behavior of a plurality of pieces of equipment. Cyclical peaks tend to be driven primarily from the way load control is done on cyclical boundaries (intervals) as compared to non-cyclical peaks which are also a concern and occur independent of the cyclical nature of the load control algorithms being implemented.